Controlling fluid volume variations of a reservoir under production转让专利
申请号 : US16371573
文献号 : US11268352B2
文献日 : 2022-03-08
发明人 : Philippe Nivlet
申请人 : Saudi Arabian Oil Company
摘要 :
权利要求 :
What is claimed is:
说明书 :
The present disclosure relates to systems and methods for controlling reservoir fluid volume with seismic data and a reservoir model.
Four-dimensional (4D) seismic data are developed by repeating seismic acquisition at different times over a reservoir, such as a reservoir under production. Acquisition and processing of the multiple seismic acquisitions are typically as similar as possible so that the differential amplitude at the reservoir level can be attributed with confidence to fluid volume change (“FVC”) (for example, pressure or saturation). Four-dimensional seismic data has good spatial coverage of the reservoir and has been used to map FVC.
In a general implementation, a computer-implemented method for controlling a hydrocarbon production system includes determining, with a hydrocarbon production system control system, a first estimate of a prior FVC detectability probability map based on a plurality of reservoir data that includes four-dimensional (4D) seismic data of a subterranean reservoir. The method further includes determining, with the hydrocarbon production system control system, a second estimate of the prior FVC detectability probability map under seismic data noise conditions. The method further includes determining, with the hydrocarbon production system control system, an updated detectable FVC probability based on the 4D seismic data. The method further includes determining, with the hydrocarbon production system control system, an updated FVC probability based on the updated detectable FVC probability and the first and second estimates of the prior FVC detectability probability maps. The method further includes generating, with the hydrocarbon production system control system, a control instruction for at least one of a fluid injection system or a hydrocarbon production assembly based on the updated FVC probability.
In an aspect combinable with the general implementation, the plurality of data further includes at least one of an average reservoir model of the subterranean reservoir, a set of equally probable history-matched reservoir models of the subterranean reservoir, or a rock physics model of a rock formation of the subterranean reservoir.
Another aspect combinable with any of the previous aspects further includes determining, with the hydrocarbon production system control system, an estimate of a probability that the 4D seismic data provides a correct diagnostic about the minimum detectable FVC.
Another aspect combinable with any of the previous aspects further includes determining, with the hydrocarbon production system control system, an estimate of a minimal detectable FVC; and generating, with the hydrocarbon production system control system, at least one estimate map of seismic detectability based at least in part on the estimate of the prior FVC detectability probability map.
In another aspect combinable with any of the previous aspects, the first estimate of the prior FVC detectability probability map is determined exclusive of seismic data noise conditions.
In another aspect combinable with any of the previous aspects, determining the updated detectable FVC probability based on the 4D seismic data includes evaluating a plurality of conditional probabilities.
In another aspect combinable with any of the previous aspects, the plurality of conditional probabilities include:
In another aspect combinable with any of the previous aspects, the control instruction for at least one of the fluid injection system or the hydrocarbon production assembly includes at least one of instructing the fluid injection system to reduce or stop circulating an fluid injectant into one or more injection wellbores; or instructing the fluid injection system to increase a rate of circulating the fluid injectant into one or more injection wellbores.
In another aspect combinable with any of the previous aspects, the control instruction for at least one of the fluid injection system or the hydrocarbon production assembly includes at least one of instructing the hydrocarbon production assembly to reduce or stop producing a hydrocarbon fluid from one or more production wellbores; or instructing one or more inflow control devices positioned on the one or more production wellbores to close.
In another aspect combinable with any of the previous aspects, the control instruction for at least one of the fluid injection system or the hydrocarbon production assembly includes at least two of instructing the fluid injection system to reduce or stop circulating an fluid injectant into one or more injection wellbores instructing the fluid injection system to increase a rate of circulating the fluid injectant into one or more injection wellbores; instructing the hydrocarbon production assembly to reduce or stop producing a hydrocarbon fluid from one or more production wellbores; or instructing one or more inflow control devices positioned on the one or more production wellbores to close.
In another general implementation, a hydrocarbon production system control system includes one or more memory modules that store a plurality of reservoir data that includes four-dimensional (4D) seismic data of a subterranean reservoir and a set of instructions, and one or more hardware processors. The one or more hardware processors is configured to execute the set of instructions to perform operations that include determining a first estimate of a prior FVC detectability probability map based on a plurality of reservoir data that includes four-dimensional (4D) seismic data of a subterranean reservoir. The operations further include determining a second estimate of the prior FVC detectability probability map under seismic data noise conditions. The operations further include determining an updated detectable FVC probability based on the 4D seismic data. The operations further include determining an updated FVC probability based on the updated detectable FVC probability and the first and second estimates of the prior FVC detectability probability maps. The operations further include generating a control instruction for at least one of a fluid injection system or a hydrocarbon production assembly based on the updated FVC probability.
In an aspect combinable with the general implementation, the plurality of data further includes at least one of an average reservoir model of the subterranean reservoir, a set of equally probable history-matched reservoir models of the subterranean reservoir, or a rock physics model of a rock formation of the subterranean reservoir.
In another aspect combinable with any of the previous aspects, the operations further include determining an estimate of a probability that the 4D seismic data provides a correct diagnostic about the minimum detectable FVC.
In another aspect combinable with any of the previous aspects, the operations further include determining an estimate of a minimal detectable FVC; and generating at least one estimate map of seismic detectability based at least in part on the estimate of the prior FVC detectability probability map.
In another aspect combinable with any of the previous aspects, the first estimate of the prior FVC detectability probability map is determined exclusive of seismic data noise conditions.
In another aspect combinable with any of the previous aspects, the operation of determining the updated detectable FVC probability based on the 4D seismic data includes evaluating a plurality of conditional probabilities.
In another aspect combinable with any of the previous aspects, the plurality of conditional probabilities include:
In another general implementation, a wellbore production system includes a fluid injection system fluidly coupled to an injection wellbore and operable to circulate an injection fluid through the injection wellbore and into a subterranean reservoir, a hydrocarbon production assembly fluidly coupled to a production wellbore and operable to circulate a hydrocarbon fluid through the production wellbore from the subterranean reservoir, and a hydrocarbon production system control system communicably coupled to the fluid injection system and the hydrocarbon production system. The hydrocarbon production system control system is configured to perform operations including determining a first estimate of a prior FVC detectability probability map based on a plurality of reservoir data that includes four-dimensional (4D) seismic data of the subterranean reservoir; determining a second estimate of the prior FVC detectability probability map under seismic data noise conditions; determining an updated detectable FVC probability based on the 4D seismic data; determining an updated FVC probability based on the updated detectable FVC probability and the first and second estimates of the prior FVC detectability probability maps; and generating a control instruction for at least one of the fluid injection system or the hydrocarbon production assembly based on the updated FVC probability.
In an aspect combinable with the general implementation, the plurality of data further includes at least one of an average reservoir model of the subterranean reservoir, a set of equally probable history-matched reservoir models of the subterranean reservoir, or a rock physics model of a rock formation of the subterranean reservoir.
In another aspect combinable with any of the previous aspects, the operations further include determining an estimate of a probability that the 4D seismic data provides a correct diagnostic about the minimum detectable FVC; determining an estimate of a minimal detectable FVC; and generating at least one estimate map of seismic detectability based at least in part on the estimate of the prior FVC detectability probability map.
In another aspect combinable with any of the previous aspects, the first estimate of the prior FVC detectability probability map is determined exclusive of seismic data noise conditions.
In another aspect combinable with any of the previous aspects, the operation of determining the updated detectable FVC probability based on the 4D seismic data includes evaluating a plurality of conditional probabilities.
In another aspect combinable with any of the previous aspects, the control instruction includes at least one of adjusting a flow rate of the injection fluid circulated by the fluid injection system through the injection wellbore; adjusting a flow rate of the hydrocarbon fluid circulated by the hydrocarbon production assembly through the production wellbore; or adjusting a position of one or more inflow control devices on the production wellbore.
Implementations according to the present disclosure may include one or more of the following features. For example, implementations according to the present disclosure may distinguish between an FVC detectability from a reservoir model and an FVC detectability from 4D seismic data. As another example, implementations according to the present disclosure integrate different data types to update a model (for example, FVC from the reservoir model) while accounting for an intensity of the update by an amount of 4D seismic data noise and an intensity of the 4D signal. Thus, implementations according to the present disclosure may account for and propagate uncertainty, such as data uncertainty or modelling uncertainty, in updating FVC probability of a reservoir.
The details of one or more implementations of the subject matter described in this disclosure are set forth in the accompanying drawings and the description. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.
As illustrated, a fluid injection assembly 114 (for example, a system of one or more pumps, one or more valves, and one or more sources of injection fluid such as CO2) is operable to circulate an injection fluid 106 through the injection wellbore 102 and into the reservoir 108 in a secondary or tertiary recovery process. For example, the injection fluid 106, such as water or gas, may be pumped through the wellbore 102 into the reservoir 108 to maintain reservoir pressure so that hydrocarbons may be produced from the production wellbore 104. In some instances, for example, separated gas from the production wellbore 104 may be reinjected into the reservoir from the wellbore 102. In some cases, such as offshore wells, water may be injected into the reservoir 108 from the wellbore 102.
As shown in this example, inflow control devices (ICDs) or inflow control valves (ICVs) 112 are positioned at intervals on the production wellbore 104. Generally, an ICD or ICV is a component installed as part of a well completion to help optimize production by equalizing reservoir inflow along a length of the wellbore. In some instances, as is shown in this figure, multiple ICDs 112 can be installed along the reservoir section of the wellbore 104. Each ICD 112 can be configured to a specific setting to partially choke flow of hydrocarbons into the production wellbore 104. Thus, the ICDs 112 can be used to delay or direct hydrocarbons into particular sections of the production wellbore 104 by reducing an annular velocity across a selected interval of the wellbore 104.
Generally, an ICV is an active component (whereas, in some aspects, an ICD is a passive component) that can be controlled from a terranean surface to maintain flow conformance into the production wellbore 104. The ICV 112 can also be controlled to stop unwanted fluids (for example, injectant fluid) from entering the wellbore 104. In some aspects, an ICV can be connected, for control purposes, to a cable that extends to the surface that provides electric conduits, hydraulic conduits, or both, to relay commands from the surface to the ICV. Alternatively, or additionally, the ICV can be controlled from a downhole controller that is located, for example, in the injection wellbore 102 or production wellbore 104. In the present disclosure, the terms ICD and ICV are generally interchangeable, as both may refer to a flow control apparatus that can be controlled from the surface.
In the example shown in
In this example implementation, a 4D seismic data acquisition system 116 is illustrated schematically. Generally, the 4D seismic data acquisition system 116 is operable to acquire 4D seismic data for the reservoir 108. For example, the 4D seismic data acquisition system 116 gathers three-dimensional seismic data at different times or over a particular time duration over the reservoir 108 to assess changes in the reservoir 108 with time. Changes may be observed in hydrocarbon or injection (or both) fluid location and saturation, pressure and temperature. The 4D seismic data acquisition system 116 can operate to acquire this seismic data at a terranean surface or in one or both of the wellbores 102 or 104.
In some aspects (as shown in
In some aspects, the hydrocarbon production system control system 999 stores or references particular data inputs to operate or adjust the operation of the fluid injection assembly 114, the 4D seismic data acquisition system 116, and the hydrocarbon production assembly 118. For example, input data can include an average reservoir model (of the reservoir 108) after history-matching to a given period of time comprising 3D geo-cellular models with time-invariant reservoir parameters (for example, facies) and time-variant parameters (for example, fluid saturations and pressure). In some aspects, the time-variant parameters are available at calendar times T0, T1, . . . , Tn (where T0 is an initial time and T1 . . . Tn are points in time beyond the initial time at times 1 . . . n). The input data can also include a set of equally probable history-matched reservoir models (of the reservoir 108) that reflect an uncertainty in the different reservoir parameters over time. The input data can also include a pre-existing rock physics model (for example, based on well log data analysis and calibrated on direct laboratory measurements of the rock formation of the reservoir 108). This model may facilitate mapping of reservoir parameters to elastic parameters. The input data can also include 4D seismic data (for example, acquired by the 4D seismic data acquisition system 116) available at times T0, . . . , Tn. This input data may be, for instance, 4D post-stack amplitudes, 4D pre-stack amplitudes, 4D inverted elastic parameters, 4D seismic attributes extracted, or a combination thereof.
In some aspects, the hydrocarbon production system control system 999 may operate to distinguish between the FVC detectability from the reservoir model (referred to as v+) and the FVC detectability from the seismic (referred to as 4D+). The former is the ability of synthetic 4D seismic associated with the history-matched model to identify FVC under noise conditions reflecting model uncertainty. The latter is the ability of measured 4D seismic data to identify FVC under estimated noise conditions depending on the reservoir model.
As described more fully with respect to
where Eq. set 1 includes the following terms:
Equation set 1 is the application of Bayes theorem to update FVC detectability after observing from seismic a detectable (4D+) or undetectable (4D−) FVC, as illustrated by
Method 200 may continue at step 204, which includes determining a minimal detectable FVC v+ and, in some aspects, prior FVC detectability probability functions pN/S(v+). These estimations vary with seismic noise as illustrated by
Then, seismic modelling is applied from this result to produce for instance the corresponding 3D seismic post-stack amplitude cubes, for instance using a 1D convolution operation of estimated reflectivities at times T0, . . . , Tn and a wavelet estimated from the 3D data. Three-dimensional pre-stack data may also be generated, for instance using a one-dimensional convolution operation of estimated reflectivities at times T0, . . . , Tn for each seismic wave reflection angle and a wavelet estimated from the 3D data for the corresponding angle. Thus, 4D seismic is the difference between each amplitude cube at times T1, . . . Tn and the amplitude at the initial time (T0).
In some aspects, 4D elastic parameter variations may be used in step 202 instead of amplitudes. In such aspects, an additional step of seismic inversion would be performed as part of step 202. In other aspects, any 4D attribute extracted from 4D post- or pre-stack amplitude volume may be used in step 202 instead of amplitudes. This includes (non-exclusively) amplitude related, phase related, time related, or frequency related attributes.
In some aspects, due to the low vertical resolution of seismic, data may not generally resolve the detailed movement of the fluid in the reservoir. However, in such cases a set of seismic attribute maps extracted from the 4D cubes over the reservoir of interest (for instance, amplitude maps, acoustic impedance maps) may be related to a cumulated vertical FVC. To achieve this objective, a statistical model (for example, a multivariate linear or non-linear regression, or a neural network model) is calibrated between local FVC at times T1, . . . , Tn and the selected set of 4D attribute maps (at the corresponding times). To check the quality of the model, predicted volumes are cross-plotted to values directly read from the reservoir model.
For example,
The minimal volume that can be quantified from seismic is referred to as minimal detectable FVC. Minimal detectable FVC can be determined by a statistical test (for example, using a Student's test) that searches if the FVC from the reservoir model given the FVC predicted by the model is significantly different from 0 or not. The data may then be split into two parts according to if the FVC is greater than or lower than this limit. A classification function is calibrated between the 4D synthetic seismic data and the detectable or non-detectable class using, for instance, a statistical methodology. This model leads to a prior detectability probability function defined under the assumption that 4D seismic are noise free.
Method 200 may estimate the minimal detectable FVC and prior detectability distributions in a similar way when 4D synthetic data are contaminated with noise described by the parameter N/S, different from 0. The 4D noise, in some aspects, is a colored noise with a similar frequency spectrum as the 4D signal and an intensity controlled by the 4D N/S.
From
Method 200 may continue at step 206, which includes determining (for example, with the hydrocarbon production system control system 999) an estimate of a probability that 4D seismic data provides a correct diagnostic about the minimum detectable FVC. For example, these probabilities of respectively true positive p(4D+/v+), false positive p(4D+/v−), false negative p(4D−/v+), and true negative p(4D−/v−) depend also on N/S. To estimate these four probabilities, when calibrating the classification function from step 204, a cross-validation (or a process such as bootstrapping) method may be used. Alternatively, the discrimination between the two classes may be tested on a test sub-population that has not been used for calibration.
Method 200 continues then by evaluating the dependency of these four probabilities on seismic noise. The classification function from step 204 is recalibrated after contaminating the 4D seismic with an increasing level of noise. This noise contamination changes the detectability threshold as shown on
Method 200 may continue at step 208, which includes generating (for example, with the hydrocarbon production system control system 999) estimate maps of seismic detectability, p(4D+), for example, at calendar times T1, . . . Tn. For example, a local 4D noise measure is estimated directly from 4D seismic data. A root-mean-square (RMS) map of 4D amplitudes is estimated in a portion of the reservoir located uphole of the reservoir. The resulting map is scaled by a factor reflecting the ratio of amplitudes from overburden to the reservoir portion. This value is then divided by the RMS of the 4D signal to obtain a N/S map at the reservoir level. For example, as shown in
This noise can then be translated in terms of minimal detectable FVC (for example, using the results from step 204). Then, using the classification function calibrated in step 204 corresponding to the observed N/S level, pN/S(4D−) and its complementary pN/S(4D−)=1−pN/S(4D+) (which is the map of local seismic detectability) are estimated. From these estimations, the most probable class map (4D+ or 4D−) can be estimated. For example,
Method 200 may continue at step 210, which includes determining (for example, with the hydrocarbon production system control system 999) estimate maps of prior FVC detectability under the reservoir model noise conditions (for example, at calendar times T1, . . . Tn). For example, with respect to the prior seismic noise, it may be assumed that the reservoir model uncertainty was described by an ensemble of equally probable reservoir models, as stated previously. By repeating, for example, the Sim2Seis process to each member of the set of the process, a set of 4D amplitude cubes are produced from which a 4D noise cube (for instance, the standard deviation of the local 4D amplitude distributions) may be calculated. Similarly to the previous step, prior N/S maps can then be estimated and these maps may be transformed to minimal detectable volume maps. Then, the classification function calibrated in step 204 can be used with the desired level of N/S. By repeating this process at each position, this process results in estimating maps of prior FVC detectable under prior noise conditions p(v+). For example,
Method 200 may continue at step 212, which includes determining (for example, with the hydrocarbon production system control system 999) an updated detectable FVC probability based on the 4D seismic data. For example, Eq. set (1) may be used to determine the updated detectable FVC probability given the seismic signal. For example,
Method 200 may continue at step 214, which includes generating (for example, with the hydrocarbon production system control system 999) an FVC probability update. For example,
p(v/4D+/−)=p(v/v+,4D+/−)p(v+/4D+/−)+p(v/v−,4D+/−)[1−p(v+/4D+/−)] Eq. 2,
where the terms p(v/v−,4D+/−) are the FVC distributions estimated from the samples greater than and less than the threshold v+. FVC distribution thus may be skewed towards the left hand side of Eq. 2. As shown in
The resulting FVC volume update is detailed in Table 1, both in terms of intensity and direction (increase or decrease), depending on the degree of match between reservoir model and 4D seismic and on the intensity of 4D noise.
Method 200 may continue at step 216, which includes generating a control instruction for at least one of a hydrocarbon production system or a fluid injection system based on the updated FVC probability. For example, in some aspects, based on the updated FVC probability, the hydrocarbon production system control system 999 may control or instruct one or both of the hydrocarbon production assembly 118 or the fluid injection system 114 to adjust operation. For example, the hydrocarbon production system control system 999 may command the fluid injection system 114 to pump more or less injection fluid 106 into the injection wellbore 102 (and into the reservoir 108). As another example, the hydrocarbon production system control system 999 may command the hydrocarbon production assembly 118 to pump more or less hydrocarbon fluid 110 into the production wellbore 104. As another example, the hydrocarbon production system control system 999 may command one or more of the ICVs or ICDs 112 to allow more or less hydrocarbon fluid 110 into the production wellbore 104 from the reservoir 108.
The system 800 includes a processor 810, a memory 820, a storage device 830, and an input/output device 840. Each of the components 810, 820, 830, and 840 are interconnected using a system bus 850. The processor 810 is capable of processing instructions for execution within the system 800. The processor may be designed using any of a number of architectures. For example, the processor 810 may be a CISC (Complex Instruction Set Computers) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor.
In one implementation, the processor 810 is a single-threaded processor. In another implementation, the processor 810 is a multi-threaded processor. The processor 810 is capable of processing instructions stored in the memory 820 or on the storage device 830 to display graphical information for a user interface on the input/output device 840.
The memory 820 stores information within the system 800. In one implementation, the memory 820 is a computer-readable medium. In one implementation, the memory 820 is a volatile memory unit. In another implementation, the memory 820 is a non-volatile memory unit.
The storage device 830 is capable of providing mass storage for the system 800. In one implementation, the storage device 830 is a computer-readable medium. In various different implementations, the storage device 830 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device.
The input/output device 840 provides input/output operations for the system 800. In one implementation, the input/output device 840 includes a keyboard or a pointing device (or both). In another implementation, the input/output device 840 includes a display unit for displaying graphical user interfaces.
Certain features described can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The apparatus can be implemented in a computer program product tangibly embodied in an information carrier, for example, in a machine-readable storage device for execution by a programmable processor. Method steps can be performed by a programmable processor executing a program of instructions to perform functions of the described implementations by operating on input data and generating output. The described features can be implemented in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages. The computer program can be deployed in any form, including as a stand-alone program or as a module, component, or subroutine.
Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as erasable programmable read-only memory (EPROM) or electrically erasable programmable read-only memory (EEPROM), solid state drives (SSDs), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (application-specific integrated circuits).
To provide for interaction with a user, the features can be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) or LED (light-emitting diode) monitor for displaying information to the user. Also, a keyboard and a pointing device such as a mouse or a trackball by which the user can provide input to the computer is provided. Additionally, such activities can be implemented via touchscreen flat-panel displays.
The features can be implemented in a control system that includes a back-end component, such as a data server, or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of them. The components of the system can be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), peer-to-peer networks (having ad-hoc or static members), grid computing infrastructures, and the Internet.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described should not be understood as requiring such separation in all implementations. The described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. For example, example operations, methods, or processes described may include more steps or fewer steps than those described. Further, the steps in such example operations, methods, or processes may be performed in different successions than that described or illustrated in the figures. Accordingly, other implementations are within the scope of the following claims.